Sort by
Refine Your Search
-
: Machine Learning Molecular Dynamics. The project involves the development and application of machine learning methods that enable a major boost of the time and length scales accessible to ab-initio/first
-
(SONATA, EP/V028626/1) and brings together expertise in microfluidics, fluid dynamics, nanoparticle engineering, and dental microbiology. Approach and Methods: Engineer in vitro models of bacterial biofilm
-
rather than the structured biofilms found in real-world environments. This project investigates how engineered surface topographies influence HGT dynamics, aiming to develop design principles for materials
-
enzymes. Mapping bacterial defence systems to infer predictive features of co-evolutionary dynamics. Impact and Outlook This project will: • Advance understanding of microbial co-evolution. • Deliver a
-
with experimental virologists to validate computational predictions Impact and Outlook: This project will uncover the untapped structural and functional potential of bovine UL-CDRs, laying the groundwork
-
PhD Studentship: Nanopore Technology for Rapid and Accurate Measurement of Antibiotic Concentrations
conditions and characterise the dynamic range and sensitivity of the nanopore sensors Develop multiplexing strategies for simultaneous detection of multiple antibiotic classes Perform proof-of-principle
-
). Design and fabricate patterned surfaces optimised for enzyme immobilisation. Assess synergistic antibiofilm efficacy under static and dynamic (flow-based) biofilm models. Apply advanced microscopy, protein
-
, with collaboration across synthetic biology, computational biology, and microbiology. The student will work within a dynamic, interdisciplinary team with access to state-of-the-art facilities and
-
Develop an active learning-driven platform for compound selection and optimisation Integrate robotic sample preparation, automated data acquisition, and computational analysis Advance five existing
-
such as, but not limited to, chemical, pharmaceutical, biochemical, or mechanical engineering; pharmaceutical sciences; materials science; or related areas. Applicants from computer science with relevant